Data-driven, multi-moment fluid modeling of Landau damping

TL;DR

This paper introduces a deep learning approach to derive fluid PDEs from kinetic plasma data, capturing Landau damping effects and enabling efficient multi-scale modeling.

Contribution

It presents a novel data-driven method to learn multi-moment fluid PDEs that incorporate kinetic effects like Landau damping from fully kinetic simulation data.

Findings

The learned PDEs accurately reproduce physical quantities from kinetic models.

The damping rate matches both kinetic simulations and linear theory.

The approach reduces computational costs for complex plasma modeling.

Abstract

Deriving governing equations of complex physical systems based on first principles can be quite challenging when there are certain unknown terms and hidden physical mechanisms in the systems. In this work, we apply a deep learning architecture to learn fluid partial differential equations (PDEs) of a plasma system based on the data acquired from a fully kinetic model. The learned multi-moment fluid PDEs are demonstrated to incorporate kinetic effects such as Landau damping. Based on the learned fluid closure, the data-driven, multi-moment fluid modeling can well reproduce all the physical quantities derived from the fully kinetic model. The calculated damping rate of Landau damping is consistent with both the fully kinetic simulation and the linear theory. The data-driven fluid modeling of PDEs for complex physical systems may be applied to improve fluid closure and reduce the computational cost of multi-scale modeling of global systems.